From base data to knowledge discovery–A life cycle approach–Using multilayer networks
Abstract Analysis of complex data sets to infer/discover meaningful information/knowledge
involves (after data collection and cleaning):(i) Modeling the data—an approach for deriving …
involves (after data collection and cleaning):(i) Modeling the data—an approach for deriving …
Degree Centrality Definition, and Its Computation for Homogeneous Multilayer Networks Using Heuristics-Based Algorithms
Centrality metrics for simple graphs/networks are well-defined and each has numerous main-
memory algorithms. M ulti L ayer N etworks (MLNs) are becoming popular for modeling …
memory algorithms. M ulti L ayer N etworks (MLNs) are becoming popular for modeling …
Stress Centrality in Heterogeneous Multilayer Networks: Heuristics-Based Detection
Centrality metrics for simple graphs are well-defined. For each centrality measure, multiple
main-memory algorithms exist for their computation. With main memory algorithms, the size …
main-memory algorithms exist for their computation. With main memory algorithms, the size …
HoMLN-SD: Substructure Discovery In Homogeneous Multilayer Networks
A Singh - 2023 - search.proquest.com
Substructure discovery is a process in data analysis and data mining that involves
identifying and extracting meaningful patterns, structures, or components within a larger …
identifying and extracting meaningful patterns, structures, or components within a larger …
[PDF][PDF] The Challenge of Finding Degree Centrality Nodes in Heterogeneous Multilayer Networks.
Complex data sets with different types of entities and relationships can be elegantly
modeled using Heterogeneous Multilayer Networks (HeMLNs), where different sets of nodes …
modeled using Heterogeneous Multilayer Networks (HeMLNs), where different sets of nodes …